Project Summary In the prodromal phase of Alzheimer's disease (AD), beta-amyloid (AB) and tau preferentially spread throughout the default mode network (DMN) leading to neuronal loss and synaptic dysfunction. Episodic memory impairments in AD are thought to arise from the loss of structural and functional connectivity between nodes of the DMN. Emerging evidence from studies with repetitive transcranial magnetic stimulation (rTMS) in young adults demonstrate that the DMN can be modulated in a manner that promotes lasting episodic memory improvement. However, several fundamental questions remain regarding the factors that govern whether rTMS is effective in patients with Alzheimer's pathology and neurodegeneration. The primary goal of this proposal is to refine our understanding of the mechanisms and therapeutic potential of rTMS for enhancing DMN integrity and episodic memory in individuals with Alzheimer's pathology. 30 patients with prodromal AD, 30 AB+ cognitively normal older adults, and 30 AB- cognitively normal older adults will each undergo 5 days of sham-controlled rTMS preceded and followed by multimodal MRI sessions and cognitive testing. rTMS targets will be established using baseline functional connectivity derived from resting state functional MRI (rsfMRI) to determine the region in lateral parietal cortex with maximal functional connectivity to the hippocampus. rsfMRI outcome measures will include functional connectivity between the stimulation site and hippocampus, and intrinsic activity within the stimulation site and hippocampus. AB, tau, and FDG positron emission tomography (PET) scans and structural MRI will be used to quantify the impact of Alzheimer's pathology, hypometabolism, and atrophy on the efficacy of rTMS treatments in each group. All outcome measures will be related to behavioral measures of episodic memory immediately and 2 weeks after the end of treatment. Aim 1 of the proposed project will establish the effects of rTMS on episodic memory in each group. Aim 2 will establish functional network effects of rTMS in each group. Aim 3 will use multivariate regression and machine learning algorithms to identify the biological features that are most useful in predicting whether an individual will benefit from rTMS. All data collection and analyses will take place at Massachusetts General Hospital and Harvard Medical School. During the completion of the project the candidate will receive training in the theory and application of TMS to modulate network function, the use of PET imaging to measure pathology in AD, clinical trial design, and the use of advanced biostatistics for biomarker development and treatment response prediction. The outcome of this research will provide insight into the individuals that are most likely to benefit from rTMS, and will inform future studies seeking to optimize rTMS treatments to improve cognition in dementia. Furthermore, the completion of this project will lay the foundation for the candidate's long-term goal of translating basic neuroimaging findings from healthy aging to direct benefits for patients with Alzheimer's disease.